Machine Learning Enabled Quickly Predicting of Detonation Properties of N-Containing Molecules for Discovering New Energetic Materials,Advanced Theory and Simulations

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Machine Learning Enabled Quickly Predicting of Detonation Properties of N-Containing Molecules for Discovering New Energetic Materials
Advanced Theory and Simulations ( IF 2.9 ) Pub Date : 2021-04-19 , DOI: 10.1002/adts.202100057
Fang Hou ₁ , Yi Ma ₂ , Zheng Hu ₁ , Shining Ding ₁ , Haihan Fu ₁ , Li Wang _{1,

3} , Xiangwen Zhang _{1,

3} , Guozhu Li _{1,

3}

Affiliation

Energetic materials are widely used in the fields of military, civil engineering, and space exploration. The discovery of new energetic materials is essential to develop next-generation technologies of weapon, mining, construction, and rocket propelling. In this study, a machine-learning-assisted method is developed for accelerating the discovery of new energetic materials via efficient prediction and quick screening. Suitable neural networks are established for accurately predicting the detonation properties of various N-containing molecules based on their structures, including density (ρ), detonation velocity (D), and detonation pressure (P). Then, the minimum database volume for high-precision extended prediction is determined. A proof-of-concept study for discovering new energetic compounds using machine learning is carried out, and 31 new N-containing molecules with outstanding detonation properties are discovered. It is expected that the development of next-generation energetic materials is greatly accelerated by the application of this strategy assisted by machine learning.

中文翻译：

机器学习能够快速预测含氮分子的爆轰特性，以发现新的高能材料

高能材料广泛应用于军事、土木工程和太空探索等领域。新能源材料的发现对于开发下一代武器、采矿、建筑和火箭推进技术至关重要。在这项研究中，开发了一种机器学习辅助方法，通过有效预测和快速筛选来加速新含能材料的发现。建立了合适的神经网络，根据各种含氮分子的结构，包括密度 ( ρ )、爆速 ( D ) 和爆压 ( P )来准确预测其爆轰特性。）。然后，确定用于高精度扩展预测的最小数据库量。开展了使用机器学习发现新含能化合物的概念验证研究，并发现了 31 种具有出色爆轰特性的新含氮分子。预计在机器学习的辅助下，该策略的应用将大大加速下一代含能材料的开发。

更新日期：2021-06-05

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